This invention relates to a wireless communication device, and in particular relates to a MIMO (Multiple-Input, Multiple-Output)-capable wireless communication device in a transmission system comprising a MIMO configuration.
Among current wireless communication systems, there is increasing interest in space division multiplexing transmission technologies (MIMO transmission systems) in which, by transmitting different data streams in parallel from a plurality of transmission antennas, the transmission capacity can be increased in proportion to the number of transmission antennas.
In a MIMO transmission system, normally signal processing to subtract replicas which occur is employed to remove the interference by other data streams from a data stream of interest (V-BLAST, MSSE-VBLAST, Zero-Forced V-BLAST).
In contrast-with such normal methods, a wireless communication device (turbo receiver device) of this invention performs iterative maximum likelihood estimation. The turb receiver device of this invention exhibits BER results which are extremely close to those for the maximum likelihood decoding (MLD) method, and at the same time alleviates the complexity of calculations.
The turbo receiver device of this invention is based on a maximum posterior probability estimation algorithm. In this turbo receiver device, after nonlinear processing, information derived from one channel refines the estimated maximum posterior probability of another channel, and similarly, information derived from the other channel refines the estimated maximum probability of the one channel.
FIG. 1 shows the configuration of a MIMO system, where TRX is a transmission station and REC is a receiver station.
Data streams D0 to DM−1 of the same number as the number of transmission antennas M pass through data modulation, D/A conversion, quadrature modulation, frequency up-conversion, and other processing by respective transmission devices TRX0 to TRXM−1, and are transmitted to the respective transmission antennas ATT0 to ATTM−1. Signals transmitted from the antennas ATT0 to ATTM−1 pass through independent fading channels hnm (m=0 to M−1, n=0 to N−1), and after space division multiplexing, are received by N receiving antennas ATR0 to ATRN−1. Signals received by the receiving antennas pass through frequency down-conversion, quadrature detection, A/D conversion and other processing by receiving devices REC0 to RECN−1, and received data streams y0 to yN−1 are generated. Each of these received data streams is in the form of M multiplexed transmitted data streams, so that by performing signal processing of all received data streams, the transmitted data streams are separated and reproduced.
Algorithms for signal processing to separate transmitted data streams D0 to DM−1 from received signals include such linear algorithms as ZF (Zero-Forcing) and MMSE which use the inverse matrix of a channel correlation matrix (see A. van Zelst, “Space Division Multiplexing Algorithms”, 10th Mediterranean Electrotechnical Conference 2000, MELECON 2000, Cyprus, May 2000, Vol. 3, pp. 1218-1221), and nonlinear algorithms of which BLAST (Bell Laboratories Layered Space-Time) is representative. In addition, there are also methods, such as MLD (Maximum Likelihood Decoding), which do not use operations on the inverse matrix of a correlation matrix (see Geert Awater, Allert van Zelst and Richard van Nee, “Reduced Complexity Space Division Multiplexing Receivers”, in Proc. IEEE VTC 2000, Tokyo, Japan, May 15-18, 2000, Vol. 2, pp. 1070-1074).
ZF (Zero-Forcing) Algorithm
If a transmitted data stream is represented by an M-dimensional complex matrix, and a received data stream by an N-dimensional complex matrix, then the following relation obtains.
                              Y          =                      H            ·            D                          ⁢                                  ⁢                  H          =                      [                                                                                                                              h                        00                                            ·                                              h                        01                                                              ⁢                                                                                  ⁢                    ⋯                    ⁢                                                                                  ⁢                                          h                                                                        0                          ⁢                          M                                                -                        1                                                                                                                                                                                    h                      10                                        ⁢                                                                                  ⁢                    ⋯                    ⁢                                                                                  ⁢                                          h                                                                        1                          ⁢                          M                                                -                        1                                                                                                                                          ⋯                                                                                                                        h                                              N                        -                        10                                                              ⁢                                                                                  ⁢                    ⋯                    ⁢                                                                                  ⁢                                          h                                              N                        -                                                  1                          ⁢                          M                                                -                        1                                                                                                                  ]                          ⁢                                  ⁢                  D          =                                    [                                                                    D                    0                                    ·                                      D                    1                                                  ⁢                                                                  ⁢                ⋯                ⁢                                                                  ⁢                                  D                                      M                    -                    1                                                              ]                                      -              1                                      ⁢                                  ⁢                  Y          =                                    [                                                                    y                    0                                    ·                                      y                    1                                                  ⁢                                                                  ⁢                ⋯                ⁢                                                                  ⁢                                  y                                      N                    -                    1                                                              ]                                      -              1                                                          (        1        )            
The ZF (Zero-Forcing) algorithm uses the following equation to estimate the transmitted data stream.{circumflex over (D)}=(H*·H)−1H*D=H+·Y  (2)
Here, H*H is called the channel correlation matrix. H+ denotes the pseudo-inverse matrix; in order for this pseudo-inverse matrix to exist, it is necessary that N≧M.
MMSE Algorithm
The MMSE (Minimum Mean Square Error) method is another linear estimation approach in which the transmitted data stream (received data vector) D is estimated based on the received data stream Y. In this MMSE algorithm, the following equation is used to determine the matrix G:ε2=E[(D−{circumflex over (D)})*(D−{circumflex over (D)})]=[(D−G·Y)*(D−G·Y)]  (3)
Then, the following equation is sued to estimate the data stream D.{circumflex over (D)}=(α·I+H*·H)−1H*·Y  (4)
Here, α is the noise dispersion, and I is a matrix which depends on G. The ZF algorithm corresponds to the MMSE algorithm when α=0.
Decision Feedback Decoding
It is expected that if the most reliable element of the transmitted vector data D is decoded and used to improve decoding of the other elements, performance will be improved. This method is called symbol cancellation, and when combined with the ZF method and MMSE method, is known as ZF-VBL and MSSE-VBL.
MLD Algorithm
The MLD algorithm is a method which does not use operations in the inverse matrix of a correlation matrix, but uses the following equation to estimate the transmitted data stream (transmission vector) D.{circumflex over (D)}=arg min∥Y−H·D∥2  (5)
Here, if the number of signal constellations of modulated data input to M antennas is Q, then there exist QM combinations of transmission vectors D. In QPSK Q=4, in 16QAM Q=16, and in 64QAM Q=64. In the MLD algorithm, candidates (replicas) of the QM transmission vectors are generated, and the operation of equation (5) is performed, and the replica the result for which is smallest is estimated to be the input data.
This inventor has proposed an iterative MAP detector which can be applied to MIMO communication systems (Alexander N. Lozhkin, “Novel Interactive MAP Detector for MIMO Communication”, Proc. WPMC '04, Sep. 12-15, 2004, Abano Terme, Italy).
FIG. 2 shows simulation results for each of the above reception methods (the ZF-VBL, MSSE-VBL, and MLD methods), indicating the BER (Bit Error Rate) for 2 Eb/N0. In simulations, two transmission antennas and two reception antennas were assumed, QPSK modulation was performed, and signals were transmitted without encoding. FIG. 2 also shows simulations results when there is no interchannel interference (ICI-free), and for REF. The ICI-free points are equivalent to AWGN obtained using simulation software. Points marked as REF are equivalent to data calculated using the following equation.
            P      err        =                  1        2            ·              erfc        ⁡                  (                                                    E                b                                            N                0                                              )                      ,      where    ⁢                  ⁢                                                      erfc              ⁡                              (                x                )                                      =                          1              -                              erf                ⁡                                  (                  x                  )                                                                                                      =                                          2                                  π                                            ⁢                                                ∫                  x                  ∞                                ⁢                                                      ⅇ                                          -                                              t                        2                                                                              ⁢                                      ⅆ                    t                                                                                          
From the simulation results, there is the problem that the BER for the ZF-VBL and MSSE-VBL methods is substantially higher than the bit error rate for the MLD method. On the other hand, although the error rate for the MLD method is low, there are the problems that the volume of computations required is huge, and that the volume of computations increases exponentially as the number of antennas is increased.
The conventional technology described in the reference by Lozhkin above proposes a method which lower the bit error rate and reduces the volume of computations, but with insufficient results.